Overview

Dataset statistics

Number of variables22
Number of observations366
Missing cells47
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.0 KiB
Average record size in memory176.3 B

Variable types

Numeric17
Categorical3
Boolean2

Alerts

MinTemp is highly overall correlated with MaxTemp and 4 other fieldsHigh correlation
MaxTemp is highly overall correlated with MinTemp and 4 other fieldsHigh correlation
Rainfall is highly overall correlated with RainTodayHigh correlation
Evaporation is highly overall correlated with MinTemp and 4 other fieldsHigh correlation
Sunshine is highly overall correlated with Humidity9am and 3 other fieldsHigh correlation
WindGustSpeed is highly overall correlated with WindSpeed3pm and 2 other fieldsHigh correlation
WindSpeed3pm is highly overall correlated with WindGustSpeedHigh correlation
Humidity9am is highly overall correlated with Evaporation and 1 other fieldsHigh correlation
Humidity3pm is highly overall correlated with MaxTemp and 3 other fieldsHigh correlation
Pressure9am is highly overall correlated with MinTemp and 2 other fieldsHigh correlation
Pressure3pm is highly overall correlated with WindGustSpeed and 2 other fieldsHigh correlation
Cloud9am is highly overall correlated with Sunshine and 2 other fieldsHigh correlation
Cloud3pm is highly overall correlated with Sunshine and 1 other fieldsHigh correlation
Temp9am is highly overall correlated with MinTemp and 4 other fieldsHigh correlation
Temp3pm is highly overall correlated with MinTemp and 4 other fieldsHigh correlation
RISK_MM is highly overall correlated with RainTomorrowHigh correlation
RainToday is highly overall correlated with RainfallHigh correlation
RainTomorrow is highly overall correlated with RISK_MMHigh correlation
WindDir9am has 31 (8.5%) missing valuesMissing
WindSpeed9am has 7 (1.9%) missing valuesMissing
Rainfall has 263 (71.9%) zerosZeros
Sunshine has 10 (2.7%) zerosZeros
WindSpeed9am has 24 (6.6%) zerosZeros
Cloud9am has 33 (9.0%) zerosZeros
Cloud3pm has 6 (1.6%) zerosZeros
RISK_MM has 263 (71.9%) zerosZeros

Reproduction

Analysis started2024-01-14 15:57:13.038622
Analysis finished2024-01-14 15:58:41.832659
Duration1 minute and 28.79 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

MinTemp
Real number (ℝ)

Distinct180
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2655738
Minimum-5.3
Maximum20.9
Zeros1
Zeros (%)0.3%
Negative51
Negative (%)13.9%
Memory size3.0 KiB
2024-01-14T15:58:42.125934image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum-5.3
5-th percentile-2.075
Q12.3
median7.45
Q312.5
95-th percentile16.5
Maximum20.9
Range26.2
Interquartile range (IQR)10.2

Descriptive statistics

Standard deviation6.0257998
Coefficient of variation (CV)0.82936324
Kurtosis-1.1142206
Mean7.2655738
Median Absolute Deviation (MAD)5.15
Skewness-0.0038109052
Sum2659.2
Variance36.310264
MonotonicityNot monotonic
2024-01-14T15:58:42.689155image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 6
 
1.6%
4.4 6
 
1.6%
15.1 5
 
1.4%
3.2 5
 
1.4%
2.3 5
 
1.4%
8.3 5
 
1.4%
-0.9 5
 
1.4%
2.4 5
 
1.4%
7.5 4
 
1.1%
10.8 4
 
1.1%
Other values (170) 316
86.3%
ValueCountFrequency (%)
-5.3 1
0.3%
-3.7 2
0.5%
-3.5 2
0.5%
-3.4 1
0.3%
-3.3 1
0.3%
-3.1 1
0.3%
-2.9 1
0.3%
-2.8 2
0.5%
-2.7 2
0.5%
-2.6 1
0.3%
ValueCountFrequency (%)
20.9 1
0.3%
19.9 1
0.3%
18.2 1
0.3%
18 1
0.3%
17.9 2
0.5%
17.6 1
0.3%
17.5 2
0.5%
17.2 2
0.5%
17.1 1
0.3%
17 1
0.3%

MaxTemp
Real number (ℝ)

Distinct187
Distinct (%)51.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.550273
Minimum7.6
Maximum35.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:43.182697image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile11.125
Q115.025
median19.65
Q325.5
95-th percentile33.2
Maximum35.8
Range28.2
Interquartile range (IQR)10.475

Descriptive statistics

Standard deviation6.6905157
Coefficient of variation (CV)0.32556821
Kurtosis-0.7451302
Mean20.550273
Median Absolute Deviation (MAD)5
Skewness0.35037734
Sum7521.4
Variance44.763
MonotonicityNot monotonic
2024-01-14T15:58:43.718064image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.8 5
 
1.4%
20.9 5
 
1.4%
12.2 5
 
1.4%
15.5 5
 
1.4%
11.6 5
 
1.4%
14.7 5
 
1.4%
14.1 5
 
1.4%
18.5 5
 
1.4%
18 5
 
1.4%
18.9 5
 
1.4%
Other values (177) 316
86.3%
ValueCountFrequency (%)
7.6 1
0.3%
8.4 1
0.3%
8.7 1
0.3%
8.8 1
0.3%
9.3 1
0.3%
9.5 1
0.3%
9.6 1
0.3%
9.7 2
0.5%
10.4 1
0.3%
10.6 1
0.3%
ValueCountFrequency (%)
35.8 1
 
0.3%
35.7 1
 
0.3%
35.2 1
 
0.3%
35 2
0.5%
34.9 1
 
0.3%
34.7 1
 
0.3%
34.2 2
0.5%
34.1 1
 
0.3%
33.9 2
0.5%
33.8 3
0.8%

Rainfall
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4284153
Minimum0
Maximum39.8
Zeros263
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:44.237163image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile8.8
Maximum39.8
Range39.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation4.2257996
Coefficient of variation (CV)2.958383
Kurtosis26.78095
Mean1.4284153
Median Absolute Deviation (MAD)0
Skewness4.5901625
Sum522.8
Variance17.857382
MonotonicityNot monotonic
2024-01-14T15:58:44.794759image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 263
71.9%
0.2 17
 
4.6%
0.6 6
 
1.6%
0.8 5
 
1.4%
0.4 5
 
1.4%
1 4
 
1.1%
6.6 3
 
0.8%
1.8 3
 
0.8%
5.2 3
 
0.8%
4.8 3
 
0.8%
Other values (37) 54
 
14.8%
ValueCountFrequency (%)
0 263
71.9%
0.2 17
 
4.6%
0.4 5
 
1.4%
0.6 6
 
1.6%
0.8 5
 
1.4%
1 4
 
1.1%
1.2 3
 
0.8%
1.4 2
 
0.5%
1.6 2
 
0.5%
1.8 3
 
0.8%
ValueCountFrequency (%)
39.8 1
0.3%
25.8 1
0.3%
22.6 1
0.3%
19.8 1
0.3%
19.2 1
0.3%
18.8 1
0.3%
17.4 2
0.5%
16.8 1
0.3%
16.2 2
0.5%
14.4 1
0.3%

Evaporation
Real number (ℝ)

Distinct55
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5218579
Minimum0.2
Maximum13.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:45.271124image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.2
Q12.2
median4.2
Q36.4
95-th percentile9.4
Maximum13.8
Range13.6
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation2.6693825
Coefficient of variation (CV)0.59032871
Kurtosis-0.17953991
Mean4.5218579
Median Absolute Deviation (MAD)2
Skewness0.66365817
Sum1655
Variance7.1256031
MonotonicityNot monotonic
2024-01-14T15:58:45.849816image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 21
 
5.7%
2.8 19
 
5.2%
1.4 15
 
4.1%
1.8 14
 
3.8%
2.6 14
 
3.8%
1.6 13
 
3.6%
6.6 13
 
3.6%
3.4 12
 
3.3%
4.4 11
 
3.0%
2.4 11
 
3.0%
Other values (45) 223
60.9%
ValueCountFrequency (%)
0.2 2
 
0.5%
0.6 5
 
1.4%
0.8 4
 
1.1%
1 3
 
0.8%
1.2 8
 
2.2%
1.4 15
4.1%
1.6 13
3.6%
1.8 14
3.8%
2 10
2.7%
2.2 21
5.7%
ValueCountFrequency (%)
13.8 1
 
0.3%
12.6 1
 
0.3%
12.4 1
 
0.3%
11.6 1
 
0.3%
11.4 1
 
0.3%
10.4 4
1.1%
10.2 1
 
0.3%
10 3
0.8%
9.6 4
1.1%
9.4 5
1.4%

Sunshine
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct114
Distinct (%)31.4%
Missing3
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean7.9093664
Minimum0
Maximum13.6
Zeros10
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:46.375446image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q15.95
median8.6
Q310.5
95-th percentile12.6
Maximum13.6
Range13.6
Interquartile range (IQR)4.55

Descriptive statistics

Standard deviation3.4815172
Coefficient of variation (CV)0.44017649
Kurtosis-0.24236655
Mean7.9093664
Median Absolute Deviation (MAD)2.2
Skewness-0.72947197
Sum2871.1
Variance12.120962
MonotonicityNot monotonic
2024-01-14T15:58:46.906823image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
2.7%
8.4 8
 
2.2%
8.1 8
 
2.2%
8.9 8
 
2.2%
9.4 7
 
1.9%
5.6 7
 
1.9%
11 7
 
1.9%
8.2 6
 
1.6%
10.8 6
 
1.6%
9.9 6
 
1.6%
Other values (104) 290
79.2%
ValueCountFrequency (%)
0 10
2.7%
0.1 1
 
0.3%
0.2 2
 
0.5%
0.3 1
 
0.3%
0.4 2
 
0.5%
0.5 2
 
0.5%
0.6 3
 
0.8%
0.7 1
 
0.3%
0.8 3
 
0.8%
0.9 2
 
0.5%
ValueCountFrequency (%)
13.6 2
0.5%
13.5 1
 
0.3%
13.3 1
 
0.3%
13.2 2
0.5%
13.1 1
 
0.3%
13 4
1.1%
12.8 2
0.5%
12.7 3
0.8%
12.6 4
1.1%
12.5 2
0.5%

WindGustDir
Categorical

Distinct16
Distinct (%)4.4%
Missing3
Missing (%)0.8%
Memory size3.0 KiB
NW
73 
NNW
44 
E
37 
WNW
35 
ENE
30 
Other values (11)
144 

Length

Max length3
Median length2
Mean length2.1625344
Min length1

Characters and Unicode

Total characters785
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNW
2nd rowENE
3rd rowNW
4th rowNW
5th rowSSE

Common Values

ValueCountFrequency (%)
NW 73
19.9%
NNW 44
12.0%
E 37
10.1%
WNW 35
9.6%
ENE 30
8.2%
ESE 23
 
6.3%
S 22
 
6.0%
N 21
 
5.7%
W 20
 
5.5%
NE 16
 
4.4%
Other values (6) 42
11.5%

Length

2024-01-14T15:58:47.471386image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nw 73
20.1%
nnw 44
12.1%
e 37
10.2%
wnw 35
9.6%
ene 30
8.3%
ese 23
 
6.3%
s 22
 
6.1%
n 21
 
5.8%
w 20
 
5.5%
ne 16
 
4.4%
Other values (6) 42
11.6%

Most occurring characters

ValueCountFrequency (%)
N 279
35.5%
W 219
27.9%
E 191
24.3%
S 96
 
12.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 785
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 279
35.5%
W 219
27.9%
E 191
24.3%
S 96
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 785
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 279
35.5%
W 219
27.9%
E 191
24.3%
S 96
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 279
35.5%
W 219
27.9%
E 191
24.3%
S 96
 
12.2%

WindGustSpeed
Real number (ℝ)

Distinct35
Distinct (%)9.6%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean39.840659
Minimum13
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:47.967878image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile20
Q131
median39
Q346
95-th percentile65
Maximum98
Range85
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.059807
Coefficient of variation (CV)0.32780098
Kurtosis1.538286
Mean39.840659
Median Absolute Deviation (MAD)8
Skewness0.84304093
Sum14502
Variance170.55856
MonotonicityNot monotonic
2024-01-14T15:58:48.464837image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
41 32
 
8.7%
39 31
 
8.5%
31 30
 
8.2%
35 24
 
6.6%
30 23
 
6.3%
46 23
 
6.3%
33 19
 
5.2%
43 18
 
4.9%
48 18
 
4.9%
50 16
 
4.4%
Other values (25) 130
35.5%
ValueCountFrequency (%)
13 2
 
0.5%
15 2
 
0.5%
17 7
 
1.9%
20 9
 
2.5%
22 11
 
3.0%
24 6
 
1.6%
26 10
 
2.7%
28 15
4.1%
30 23
6.3%
31 30
8.2%
ValueCountFrequency (%)
98 1
 
0.3%
85 1
 
0.3%
83 1
 
0.3%
80 1
 
0.3%
78 2
 
0.5%
76 2
 
0.5%
70 4
1.1%
69 1
 
0.3%
67 1
 
0.3%
65 6
1.6%

WindDir9am
Categorical

Distinct16
Distinct (%)4.8%
Missing31
Missing (%)8.5%
Memory size3.0 KiB
SE
47 
SSE
40 
NNW
36 
N
31 
NW
30 
Other values (11)
151 

Length

Max length3
Median length2
Mean length2.2119403
Min length1

Characters and Unicode

Total characters741
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSW
2nd rowE
3rd rowN
4th rowWNW
5th rowSSE

Common Values

ValueCountFrequency (%)
SE 47
12.8%
SSE 40
10.9%
NNW 36
9.8%
N 31
8.5%
NW 30
8.2%
ESE 29
7.9%
S 27
7.4%
E 22
6.0%
SSW 17
 
4.6%
WNW 16
 
4.4%
Other values (6) 40
10.9%
(Missing) 31
8.5%

Length

2024-01-14T15:58:49.012105image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
se 47
14.0%
sse 40
11.9%
nnw 36
10.7%
n 31
9.3%
nw 30
9.0%
ese 29
8.7%
s 27
8.1%
e 22
6.6%
ssw 17
 
5.1%
wnw 16
 
4.8%
Other values (6) 40
11.9%

Most occurring characters

ValueCountFrequency (%)
S 229
30.9%
E 195
26.3%
N 177
23.9%
W 140
18.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 741
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 229
30.9%
E 195
26.3%
N 177
23.9%
W 140
18.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 741
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 229
30.9%
E 195
26.3%
N 177
23.9%
W 140
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 229
30.9%
E 195
26.3%
N 177
23.9%
W 140
18.9%

WindDir3pm
Categorical

Distinct16
Distinct (%)4.4%
Missing1
Missing (%)0.3%
Memory size3.0 KiB
NW
61 
WNW
61 
NNW
47 
N
30 
ESE
27 
Other values (11)
139 

Length

Max length3
Median length3
Mean length2.2712329
Min length1

Characters and Unicode

Total characters829
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNW
2nd rowW
3rd rowNNE
4th rowW
5th rowESE

Common Values

ValueCountFrequency (%)
NW 61
16.7%
WNW 61
16.7%
NNW 47
12.8%
N 30
8.2%
ESE 27
7.4%
W 26
7.1%
E 17
 
4.6%
NE 15
 
4.1%
S 14
 
3.8%
NNE 14
 
3.8%
Other values (6) 53
14.5%

Length

2024-01-14T15:58:49.584081image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nw 61
16.7%
wnw 61
16.7%
nnw 47
12.9%
n 30
8.2%
ese 27
7.4%
w 26
7.1%
e 17
 
4.7%
ne 15
 
4.1%
s 14
 
3.8%
nne 14
 
3.8%
Other values (6) 53
14.5%

Most occurring characters

ValueCountFrequency (%)
N 302
36.4%
W 288
34.7%
E 145
17.5%
S 94
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 829
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 302
36.4%
W 288
34.7%
E 145
17.5%
S 94
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 829
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 302
36.4%
W 288
34.7%
E 145
17.5%
S 94
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 302
36.4%
W 288
34.7%
E 145
17.5%
S 94
 
11.3%

WindSpeed9am
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)6.1%
Missing7
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean9.6518106
Minimum0
Maximum41
Zeros24
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:50.070883image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median7
Q313
95-th percentile28
Maximum41
Range41
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.951929
Coefficient of variation (CV)0.82387951
Kurtosis1.538881
Mean9.6518106
Median Absolute Deviation (MAD)3
Skewness1.3716119
Sum3465
Variance63.233174
MonotonicityNot monotonic
2024-01-14T15:58:50.572019image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6 76
20.8%
7 53
14.5%
2 36
9.8%
9 29
 
7.9%
4 26
 
7.1%
0 24
 
6.6%
11 19
 
5.2%
13 17
 
4.6%
20 14
 
3.8%
15 13
 
3.6%
Other values (12) 52
14.2%
ValueCountFrequency (%)
0 24
 
6.6%
2 36
9.8%
4 26
 
7.1%
6 76
20.8%
7 53
14.5%
9 29
 
7.9%
11 19
 
5.2%
13 17
 
4.6%
15 13
 
3.6%
17 8
 
2.2%
ValueCountFrequency (%)
41 1
 
0.3%
39 1
 
0.3%
35 1
 
0.3%
33 1
 
0.3%
31 6
1.6%
30 5
1.4%
28 4
1.1%
26 6
1.6%
24 7
1.9%
22 4
1.1%

WindSpeed3pm
Real number (ℝ)

Distinct26
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.986339
Minimum0
Maximum52
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:51.066535image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q111
median17
Q324
95-th percentile32.5
Maximum52
Range52
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.8569966
Coefficient of variation (CV)0.4924291
Kurtosis0.23339076
Mean17.986339
Median Absolute Deviation (MAD)7
Skewness0.59614971
Sum6583
Variance78.446388
MonotonicityNot monotonic
2024-01-14T15:58:51.563063image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
9 30
 
8.2%
7 30
 
8.2%
11 30
 
8.2%
13 30
 
8.2%
20 27
 
7.4%
24 24
 
6.6%
15 24
 
6.6%
19 23
 
6.3%
17 22
 
6.0%
26 22
 
6.0%
Other values (16) 104
28.4%
ValueCountFrequency (%)
0 1
 
0.3%
2 1
 
0.3%
4 2
 
0.5%
6 19
5.2%
7 30
8.2%
9 30
8.2%
11 30
8.2%
13 30
8.2%
15 24
6.6%
17 22
6.0%
ValueCountFrequency (%)
52 1
 
0.3%
50 1
 
0.3%
48 1
 
0.3%
41 2
 
0.5%
39 1
 
0.3%
37 3
 
0.8%
35 3
 
0.8%
33 7
1.9%
31 8
2.2%
30 15
4.1%

Humidity9am
Real number (ℝ)

Distinct60
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.035519
Minimum36
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:52.057247image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile47.25
Q164
median72
Q381
95-th percentile94
Maximum99
Range63
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.137058
Coefficient of variation (CV)0.18236917
Kurtosis-0.17668014
Mean72.035519
Median Absolute Deviation (MAD)8
Skewness-0.1414572
Sum26365
Variance172.5823
MonotonicityNot monotonic
2024-01-14T15:58:52.611883image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 19
 
5.2%
74 17
 
4.6%
70 16
 
4.4%
76 14
 
3.8%
82 12
 
3.3%
80 12
 
3.3%
60 12
 
3.3%
68 11
 
3.0%
73 11
 
3.0%
81 11
 
3.0%
Other values (50) 231
63.1%
ValueCountFrequency (%)
36 1
 
0.3%
38 1
 
0.3%
41 1
 
0.3%
42 1
 
0.3%
43 3
0.8%
44 5
1.4%
45 3
0.8%
46 1
 
0.3%
47 3
0.8%
48 1
 
0.3%
ValueCountFrequency (%)
99 9
2.5%
97 3
 
0.8%
96 1
 
0.3%
95 4
 
1.1%
94 3
 
0.8%
93 4
 
1.1%
92 10
2.7%
91 4
 
1.1%
90 4
 
1.1%
89 4
 
1.1%

Humidity3pm
Real number (ℝ)

Distinct74
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.519126
Minimum13
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:53.145827image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile20
Q132.25
median43
Q355
95-th percentile75.5
Maximum96
Range83
Interquartile range (IQR)22.75

Descriptive statistics

Standard deviation16.850947
Coefficient of variation (CV)0.37851029
Kurtosis0.04504458
Mean44.519126
Median Absolute Deviation (MAD)11
Skewness0.59473089
Sum16294
Variance283.95443
MonotonicityNot monotonic
2024-01-14T15:58:54.353898image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34 15
 
4.1%
48 13
 
3.6%
49 11
 
3.0%
42 11
 
3.0%
35 11
 
3.0%
44 11
 
3.0%
25 10
 
2.7%
43 10
 
2.7%
51 10
 
2.7%
40 10
 
2.7%
Other values (64) 254
69.4%
ValueCountFrequency (%)
13 1
 
0.3%
14 1
 
0.3%
15 4
1.1%
16 3
0.8%
17 1
 
0.3%
18 4
1.1%
20 6
1.6%
21 1
 
0.3%
22 6
1.6%
23 4
1.1%
ValueCountFrequency (%)
96 1
 
0.3%
94 1
 
0.3%
93 1
 
0.3%
90 1
 
0.3%
88 1
 
0.3%
87 1
 
0.3%
86 3
0.8%
85 2
0.5%
82 1
 
0.3%
80 1
 
0.3%

Pressure9am
Real number (ℝ)

Distinct190
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1019.709
Minimum996.5
Maximum1035.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:54.862546image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum996.5
5-th percentile1007.9
Q11015.35
median1020.15
Q31024.475
95-th percentile1029.975
Maximum1035.7
Range39.2
Interquartile range (IQR)9.125

Descriptive statistics

Standard deviation6.6862116
Coefficient of variation (CV)0.00655698
Kurtosis-0.043636253
Mean1019.709
Median Absolute Deviation (MAD)4.45
Skewness-0.34859082
Sum373213.5
Variance44.705425
MonotonicityNot monotonic
2024-01-14T15:58:55.448749image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025.7 7
 
1.9%
1023.2 6
 
1.6%
1020.8 5
 
1.4%
1018 5
 
1.4%
1017.4 5
 
1.4%
1024.4 5
 
1.4%
1027.8 5
 
1.4%
1019.7 4
 
1.1%
1020.6 4
 
1.1%
1016.8 4
 
1.1%
Other values (180) 316
86.3%
ValueCountFrequency (%)
996.5 1
0.3%
999.4 1
0.3%
1002.1 1
0.3%
1003.2 1
0.3%
1004 1
0.3%
1004.9 1
0.3%
1005.1 1
0.3%
1005.5 1
0.3%
1006.3 2
0.5%
1006.6 1
0.3%
ValueCountFrequency (%)
1035.7 1
0.3%
1034.3 1
0.3%
1033.6 1
0.3%
1033.5 1
0.3%
1033.2 1
0.3%
1032.9 1
0.3%
1032.3 1
0.3%
1032.2 2
0.5%
1032.1 1
0.3%
1031.4 1
0.3%

Pressure3pm
Real number (ℝ)

Distinct193
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1016.8104
Minimum996.8
Maximum1033.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:55.984740image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum996.8
5-th percentile1006.15
Q11012.8
median1017.4
Q31021.475
95-th percentile1026.75
Maximum1033.2
Range36.4
Interquartile range (IQR)8.675

Descriptive statistics

Standard deviation6.4694224
Coefficient of variation (CV)0.0063624669
Kurtosis-0.0030298162
Mean1016.8104
Median Absolute Deviation (MAD)4.4
Skewness-0.29524676
Sum372152.6
Variance41.853426
MonotonicityNot monotonic
2024-01-14T15:58:56.567952image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1019.3 7
 
1.9%
1016.8 5
 
1.4%
1019.2 5
 
1.4%
1012.8 4
 
1.1%
1022.1 4
 
1.1%
1023.5 4
 
1.1%
1019.1 4
 
1.1%
1014.1 4
 
1.1%
1014.3 4
 
1.1%
1018.6 4
 
1.1%
Other values (183) 321
87.7%
ValueCountFrequency (%)
996.8 1
0.3%
997.5 1
0.3%
997.7 1
0.3%
998.9 1
0.3%
1001.3 1
0.3%
1001.5 1
0.3%
1001.8 1
0.3%
1002.3 1
0.3%
1003 1
0.3%
1003.3 1
0.3%
ValueCountFrequency (%)
1033.2 1
0.3%
1031.9 1
0.3%
1031.7 1
0.3%
1031.1 1
0.3%
1030 1
0.3%
1029.6 1
0.3%
1028.9 2
0.5%
1028.7 1
0.3%
1028 1
0.3%
1027.9 1
0.3%

Cloud9am
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8907104
Minimum0
Maximum8
Zeros33
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:57.055207image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3.5
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.9561306
Coefficient of variation (CV)0.75979199
Kurtosis-1.7168408
Mean3.8907104
Median Absolute Deviation (MAD)2.5
Skewness0.080433261
Sum1424
Variance8.738708
MonotonicityNot monotonic
2024-01-14T15:58:57.528697image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 116
31.7%
7 86
23.5%
8 40
 
10.9%
0 33
 
9.0%
6 25
 
6.8%
5 23
 
6.3%
2 17
 
4.6%
3 17
 
4.6%
4 9
 
2.5%
ValueCountFrequency (%)
0 33
 
9.0%
1 116
31.7%
2 17
 
4.6%
3 17
 
4.6%
4 9
 
2.5%
5 23
 
6.3%
6 25
 
6.8%
7 86
23.5%
8 40
 
10.9%
ValueCountFrequency (%)
8 40
 
10.9%
7 86
23.5%
6 25
 
6.8%
5 23
 
6.3%
4 9
 
2.5%
3 17
 
4.6%
2 17
 
4.6%
1 116
31.7%
0 33
 
9.0%

Cloud3pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0245902
Minimum0
Maximum8
Zeros6
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:57.976013image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median4
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.6662676
Coefficient of variation (CV)0.66249418
Kurtosis-1.6285323
Mean4.0245902
Median Absolute Deviation (MAD)3
Skewness0.072897527
Sum1473
Variance7.1089827
MonotonicityNot monotonic
2024-01-14T15:58:58.447733image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 109
29.8%
7 83
22.7%
2 35
 
9.6%
6 33
 
9.0%
8 28
 
7.7%
5 27
 
7.4%
3 24
 
6.6%
4 21
 
5.7%
0 6
 
1.6%
ValueCountFrequency (%)
0 6
 
1.6%
1 109
29.8%
2 35
 
9.6%
3 24
 
6.6%
4 21
 
5.7%
5 27
 
7.4%
6 33
 
9.0%
7 83
22.7%
8 28
 
7.7%
ValueCountFrequency (%)
8 28
 
7.7%
7 83
22.7%
6 33
 
9.0%
5 27
 
7.4%
4 21
 
5.7%
3 24
 
6.6%
2 35
 
9.6%
1 109
29.8%
0 6
 
1.6%

Temp9am
Real number (ℝ)

Distinct178
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.35847
Minimum0.1
Maximum24.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:58:58.950228image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile3.525
Q17.625
median12.55
Q317
95-th percentile21.375
Maximum24.7
Range24.6
Interquartile range (IQR)9.375

Descriptive statistics

Standard deviation5.6308321
Coefficient of variation (CV)0.45562535
Kurtosis-0.95699809
Mean12.35847
Median Absolute Deviation (MAD)4.65
Skewness-0.021783278
Sum4523.2
Variance31.706271
MonotonicityNot monotonic
2024-01-14T15:58:59.512592image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 6
 
1.6%
7.9 6
 
1.6%
5.5 6
 
1.6%
17.4 5
 
1.4%
17.5 5
 
1.4%
6.2 5
 
1.4%
15.3 5
 
1.4%
6.5 5
 
1.4%
15.8 5
 
1.4%
15.7 4
 
1.1%
Other values (168) 314
85.8%
ValueCountFrequency (%)
0.1 1
0.3%
0.8 1
0.3%
1 1
0.3%
1.2 1
0.3%
1.3 1
0.3%
1.4 2
0.5%
1.8 1
0.3%
2.1 1
0.3%
2.4 1
0.3%
2.6 1
0.3%
ValueCountFrequency (%)
24.7 1
0.3%
24.5 1
0.3%
23.8 1
0.3%
23.6 1
0.3%
23.4 1
0.3%
23 2
0.5%
22.8 1
0.3%
22.5 1
0.3%
22.4 1
0.3%
22.2 2
0.5%

Temp3pm
Real number (ℝ)

Distinct200
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.230874
Minimum5.1
Maximum34.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:59:00.012429image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile9.525
Q114.15
median18.55
Q324
95-th percentile31.6
Maximum34.5
Range29.4
Interquartile range (IQR)9.85

Descriptive statistics

Standard deviation6.640346
Coefficient of variation (CV)0.3452961
Kurtosis-0.66652448
Mean19.230874
Median Absolute Deviation (MAD)4.85
Skewness0.30350966
Sum7038.5
Variance44.094195
MonotonicityNot monotonic
2024-01-14T15:59:00.567318image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1 5
 
1.4%
16.3 5
 
1.4%
14.5 4
 
1.1%
30.7 4
 
1.1%
18.6 4
 
1.1%
12.3 4
 
1.1%
13.9 4
 
1.1%
18.8 4
 
1.1%
16.6 4
 
1.1%
20 4
 
1.1%
Other values (190) 324
88.5%
ValueCountFrequency (%)
5.1 1
0.3%
5.7 1
0.3%
6.9 1
0.3%
7.1 1
0.3%
7.2 1
0.3%
7.3 1
0.3%
7.4 1
0.3%
7.8 1
0.3%
8 1
0.3%
8.1 1
0.3%
ValueCountFrequency (%)
34.5 1
0.3%
34.3 1
0.3%
34.1 1
0.3%
34 1
0.3%
33.6 1
0.3%
33.1 1
0.3%
32.8 1
0.3%
32.7 2
0.5%
32.3 1
0.3%
32.2 2
0.5%

RainToday
Boolean

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size494.0 B
False
300 
True
66 
ValueCountFrequency (%)
False 300
82.0%
True 66
 
18.0%
2024-01-14T15:59:01.011289image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

RISK_MM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4284153
Minimum0
Maximum39.8
Zeros263
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-01-14T15:59:01.483591image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2
95-th percentile8.8
Maximum39.8
Range39.8
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation4.2257996
Coefficient of variation (CV)2.958383
Kurtosis26.78095
Mean1.4284153
Median Absolute Deviation (MAD)0
Skewness4.5901625
Sum522.8
Variance17.857382
MonotonicityNot monotonic
2024-01-14T15:59:02.070126image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 263
71.9%
0.2 17
 
4.6%
0.6 6
 
1.6%
0.4 5
 
1.4%
0.8 5
 
1.4%
1 4
 
1.1%
3.4 3
 
0.8%
2 3
 
0.8%
1.8 3
 
0.8%
1.2 3
 
0.8%
Other values (37) 54
 
14.8%
ValueCountFrequency (%)
0 263
71.9%
0.2 17
 
4.6%
0.4 5
 
1.4%
0.6 6
 
1.6%
0.8 5
 
1.4%
1 4
 
1.1%
1.2 3
 
0.8%
1.4 2
 
0.5%
1.6 2
 
0.5%
1.8 3
 
0.8%
ValueCountFrequency (%)
39.8 1
0.3%
25.8 1
0.3%
22.6 1
0.3%
19.8 1
0.3%
19.2 1
0.3%
18.8 1
0.3%
17.4 2
0.5%
16.8 1
0.3%
16.2 2
0.5%
14.4 1
0.3%
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size494.0 B
False
300 
True
66 
ValueCountFrequency (%)
False 300
82.0%
True 66
 
18.0%
2024-01-14T15:59:02.518673image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Interactions

2024-01-14T15:58:35.692765image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:14.752519image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:19.042417image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:23.514762image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:29.080411image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:36.140731image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:41.727678image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:46.959285image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
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2024-01-14T15:57:27.491416image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:32.987921image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:40.057919image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:45.330932image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:51.028380image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:55.405147image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:00.421945image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:05.870080image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:12.014407image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:17.726437image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:23.441326image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:27.286343image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:31.236816image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:34.613780image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:38.424173image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:18.221780image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:22.211177image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:27.840836image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:33.304369image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:40.401480image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:45.666044image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:51.362096image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:55.639646image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:00.763274image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:06.203764image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:12.369265image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:18.074915image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:23.785080image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:27.508258image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:31.438262image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:34.874943image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:38.758960image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:18.431946image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:22.547354image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:28.185320image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:35.142751image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:40.743504image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:46.000143image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:51.694607image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:55.863419image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:01.101095image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:06.541447image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:12.719239image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:18.429236image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:24.002319image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:27.727913image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:31.649257image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:35.089946image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:39.076235image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:18.629403image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:22.862237image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:28.512516image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:35.465782image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:41.063495image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:46.309168image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:51.995370image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:56.060191image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:01.418425image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:06.868156image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:13.050519image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:18.764087image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:24.193946image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:27.927742image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:31.832610image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:35.278571image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:39.385407image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:18.832855image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:23.178395image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:28.741621image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:35.800128image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:41.387737image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:46.622718image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:52.306491image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:57:56.265073image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:01.745186image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:07.193592image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:13.391171image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:19.102559image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:24.401663image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:28.130931image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:32.019432image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-01-14T15:58:35.471659image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2024-01-14T15:59:02.832559image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
MinTempMaxTempRainfallEvaporationSunshineWindGustSpeedWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRISK_MMWindGustDirWindDir9amWindDir3pmRainTodayRainTomorrow
MinTemp1.0000.7610.2090.6510.1010.2290.198-0.084-0.222-0.068-0.501-0.4970.2200.1380.9200.7330.2220.1740.0630.0990.2050.287
MaxTemp0.7611.000-0.1210.7100.4760.120-0.146-0.198-0.338-0.539-0.309-0.390-0.167-0.1050.8780.9910.0090.1320.0900.0680.0000.094
Rainfall0.209-0.1211.000-0.104-0.2290.1520.2240.0990.2350.387-0.364-0.2850.3000.1770.046-0.1320.2530.0000.0000.1200.7460.169
Evaporation0.6510.710-0.1041.0000.4000.3210.1230.038-0.546-0.444-0.402-0.416-0.132-0.0990.7200.693-0.0010.1370.0000.0000.0000.000
Sunshine0.1010.476-0.2290.4001.0000.105-0.0880.023-0.522-0.734-0.032-0.068-0.697-0.6470.2770.493-0.4460.0700.0000.0630.1570.379
WindGustSpeed0.2290.1200.1520.3210.1051.0000.4060.676-0.363-0.095-0.540-0.5160.0090.0190.2610.0870.1780.1210.1310.0310.1890.322
WindSpeed9am0.198-0.1460.2240.123-0.0880.4061.0000.354-0.2800.198-0.283-0.1830.189-0.0110.118-0.1690.0620.0890.1500.1090.2640.142
WindSpeed3pm-0.084-0.1980.0990.0380.0230.6760.3541.000-0.2310.018-0.356-0.3220.007-0.002-0.048-0.2180.0050.1530.1390.1240.0510.188
Humidity9am-0.222-0.3380.235-0.546-0.522-0.363-0.280-0.2311.0000.4930.1580.1520.3940.262-0.418-0.3280.2260.0000.0000.0000.1890.210
Humidity3pm-0.068-0.5390.387-0.444-0.734-0.0950.1980.0180.4931.000-0.0360.0440.5680.480-0.284-0.5740.3770.0000.0000.0610.3190.426
Pressure9am-0.501-0.309-0.364-0.402-0.032-0.540-0.283-0.3560.158-0.0361.0000.970-0.163-0.145-0.472-0.283-0.3280.1020.1390.0900.3320.346
Pressure3pm-0.497-0.390-0.285-0.416-0.068-0.516-0.183-0.3220.1520.0440.9701.000-0.134-0.149-0.504-0.367-0.3540.1240.1110.1100.2640.366
Cloud9am0.220-0.1670.300-0.132-0.6970.0090.1890.0070.3940.568-0.163-0.1341.0000.5440.031-0.1890.3440.1000.0640.0600.3070.297
Cloud3pm0.138-0.1050.177-0.099-0.6470.019-0.011-0.0020.2620.480-0.145-0.1490.5441.0000.064-0.1360.4650.0820.0000.0390.1880.427
Temp9am0.9200.8780.0460.7200.2770.2610.118-0.048-0.418-0.284-0.472-0.5040.0310.0641.0000.8540.1570.1340.0000.0900.0950.150
Temp3pm0.7330.991-0.1320.6930.4930.087-0.169-0.218-0.328-0.574-0.283-0.367-0.189-0.1360.8541.000-0.0190.1360.0000.0920.0000.109
RISK_MM0.2220.0090.253-0.001-0.4460.1780.0620.0050.2260.377-0.328-0.3540.3440.4650.157-0.0191.0000.0000.1190.0790.1390.746
WindGustDir0.1740.1320.0000.1370.0700.1210.0890.1530.0000.0000.1020.1240.1000.0820.1340.1360.0001.0000.1400.2830.0820.179
WindDir9am0.0630.0900.0000.0000.0000.1310.1500.1390.0000.0000.1390.1110.0640.0000.0000.0000.1190.1401.0000.1210.1640.160
WindDir3pm0.0990.0680.1200.0000.0630.0310.1090.1240.0000.0610.0900.1100.0600.0390.0900.0920.0790.2830.1211.0000.1840.000
RainToday0.2050.0000.7460.0000.1570.1890.2640.0510.1890.3190.3320.2640.3070.1880.0950.0000.1390.0820.1640.1841.0000.150
RainTomorrow0.2870.0940.1690.0000.3790.3220.1420.1880.2100.4260.3460.3660.2970.4270.1500.1090.7460.1790.1600.0000.1501.000

Missing values

2024-01-14T15:58:39.952702image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:58:40.944956image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-01-14T15:58:41.553847image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRISK_MMRainTomorrow
08.024.30.03.46.3NW30.0SWNW6.02068291019.71015.07714.423.6No3.6Yes
114.026.93.64.49.7ENE39.0EW4.01780361012.41008.45317.525.7Yes3.6Yes
213.723.43.65.83.3NW85.0NNNE6.0682691009.51007.28715.420.2Yes39.8Yes
313.315.539.87.29.1NW54.0WNWW30.02462561005.51007.02713.514.1Yes2.8Yes
47.616.12.85.610.6SSE50.0SSEESE20.02868491018.31018.57711.115.4Yes0.0No
56.216.90.05.88.2SE44.0SEE20.02470571023.81021.77510.914.8No0.2No
66.118.20.24.28.4SE43.0SEESE19.02663471024.61022.24612.417.3No0.0No
78.317.00.05.64.6E41.0SEE11.02465571026.21024.26712.115.5No0.0No
88.819.50.04.04.1S48.0EENE19.01770481026.11022.77714.118.9No16.2Yes
98.422.816.25.47.7E31.0SESE7.0682321024.11020.77113.321.7Yes0.0No
MinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRISK_MMRainTomorrow
3563.415.00.84.811.7S70.0SS35.03743241023.41023.1158.314.3No0.0No
3573.218.00.07.412.2SSE48.0SSES26.01547251026.61022.8129.116.3No0.0No
3580.920.70.05.48.4NNW39.0SSEN2.01771291023.21018.4389.419.1No0.0No
3593.325.50.05.210.8N43.0NNNW4.01957161018.81014.60312.024.8No0.0No
3607.926.10.06.83.5NNW43.0NaNWNW0.01945201017.61014.25816.325.9No0.0No
3619.030.70.07.612.1NNW76.0SSENW7.05038151016.11010.81320.430.0No0.0No
3627.128.40.011.612.7N48.0NNWNNW2.01945221020.01016.90117.228.2No0.0No
36312.519.90.08.45.3ESE43.0ENEENE11.0963471024.01022.83214.518.3No0.0No
36412.526.90.05.07.1NW46.0SSWWNW6.02869391021.01016.26715.825.9No0.0No
36512.330.20.06.012.6NW78.0NWWNW31.03543131009.61009.21123.828.6No0.0No